A115: Revision of the Chinese Version of Physical Self-Description Questionnaire-Short for Middle School Students

Purpose: The Physical Self-Description Questionnaire -Short (PSDQ-S) is one of the world\u27s recognized effective tools for measuring Physical Self-Concept (PSC). However, Latent profile analysis of the physical self-description among Chinese adolescents only supported the 3-dimensional model of PSDQ-S, that is, it could only distinguish the Physical Activity (PA), Appearance, and Body Fat of Chinese children and adolescents, but could not effectively distinguish the 8 dimensions of Coordination, Flexibility, Strength, Endurance, Sport, Global Physical, Health, and Global Esteem. It cannot meet the needs of PSC measurement in the field of sports psychology in China. The purpose of this study was to revise the PSDQ-S for Chinese middle school students, and to test its reliability, validity, and gender measurement equivalence in Chinese middle school students. Methods: A stratified random cluster sampling method was used to conduct a questionnaire survey on the Chinese version of PSDQ-S. 2505 middle school students in grades 7-12 (12-18 years old) were selected from seven administrative geographic regions of North China, Northeast China, East China, Central China, South China, Southwest and Northwest China, among which 1239 were male subjects, with an average age of (15.07±1.93) years, Female subjects were 1266, with an average age of (15.02±2.04) years. SPSS 24.0 and Mplus 8.3 were used for data analysis. Results: Eight common factors were extracted by exploratory factor analysis, and the cumulative variance interpretation rate was 79.45%. Confirmatory factor analysis support 8 factor model hypothesis (χ2 / df = 1.846, CFI = 0.939, TLI = 0.929, SRMR = 0.050, RMSEA = 0.061). The 8 dimensions were Physical Activity, Appearance, Body Fat, Flexibility & Coordination, Endurance, Sport, Global Physical and Health. The average variance extraction of each factor of the Chinese version PSDQ-S convergence validity index was greater than 0.50, and the combination reliability was greater than 0.60. The gender equivalence hypothesis was established. Conclusions: The revised Chinese version of PSDQ-S has good reliability, validity, and gender equivalence. It can be used as a measurement tool of PSC of Chinese middle school students

Gaussian variational method to Fermi Hubbard model in one and two dimensions

The study of ground-state properties of the Fermi-Hubbard model is a long-lasting task in the research of strongly correlated systems. Owing to the exponentially growing complexity of the system, a quantitative analysis usually demands high computational cost and is restricted to small samples, especially in two or higher dimensions. Here, we introduce a variational method in the frame of fermionic Gaussian states, and obtain the ground states of one- and two-dimensional attractive Hubbard models via imaginary-time evolution. We calculate the total energy and benchmark the results in a wide range of interaction strength and filling factor with those obtained via exact two-body results, the density matrix renormalization group based on matrix product states (MPS), and projector Quantum Monte Carlo (QMC) method. For both 1D and 2D cases, the Gaussian variational method presents accurate results for total energy with a maximum systematic error ~4% in the intermediate interaction region. The accuracy of these results has negligible dependence on the system size. We further calculate the double occupancy and find excellent agreement with MPS and QMC, as well as the experimental results of cold quantum gases in optical lattices. The results suggest that the Gaussian pairing state is a good approximation to the ground states of attractive Hubbard model, in particular in the strong and weak coupling limits. Moreover, we generalize the method to the attractive Hubbard model with a finite spin-polarization, which can be mapped to the repulsive interaction case via particle-hole transformation, and obtain accurate results of ground state energy and double occupancy. Our work demonstrates the ability of the Gaussian variational method to extract ground state properties of strongly correlated many-body systems with negligible computational cost, especially of large size and in higher dimensions.Comment: 9 pages, 6 figure

Video Action Recognition with Attentive Semantic Units

Visual-Language Models (VLMs) have significantly advanced action video recognition. Supervised by the semantics of action labels, recent works adapt the visual branch of VLMs to learn video representations. Despite the effectiveness proved by these works, we believe that the potential of VLMs has yet to be fully harnessed. In light of this, we exploit the semantic units (SU) hiding behind the action labels and leverage their correlations with fine-grained items in frames for more accurate action recognition. SUs are entities extracted from the language descriptions of the entire action set, including body parts, objects, scenes, and motions. To further enhance the alignments between visual contents and the SUs, we introduce a multi-region module (MRA) to the visual branch of the VLM. The MRA allows the perception of region-aware visual features beyond the original global feature. Our method adaptively attends to and selects relevant SUs with visual features of frames. With a cross-modal decoder, the selected SUs serve to decode spatiotemporal video representations. In summary, the SUs as the medium can boost discriminative ability and transferability. Specifically, in fully-supervised learning, our method achieved 87.8% top-1 accuracy on Kinetics-400. In K=2 few-shot experiments, our method surpassed the previous state-of-the-art by +7.1% and +15.0% on HMDB-51 and UCF-101, respectively.Comment: Accepted at ICCV 202

What Should Streamers Communicate in Livestream E-Commerce? The Effects of Social Interactions on Live Streaming Performance

Compared with traditional e-commerce, livestreaming e-commerce is characterized by direct and intimate communication between streamers and consumers that stimulates instant social interactions. This study focuses on streamers’ three types of information exchange (i.e., product information, social conversation, and social solicitation) and examines their roles in driving both short-term and long-term livestreaming performance (i.e., sales and customer base growth). We find that the informational role of product information (nonpromotional and promotional) is beneficial not only to sales performance, but also to the growth of the customer base. We also find that social conversation has a relationship-building effect that positively impacts both sales and customer base growth, whereas social solicitation has both a relationship-building and a relationship-straining effect that positively affects customer base growth but can hurt sales. Furthermore, our results show that streamers’ social interactions with consumers can stimulate consumer engagement in different ways, leading to different effects on livestreaming performance

PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer

We present PBFormer, an efficient yet powerful scene text detector that unifies the transformer with a novel text shape representation Polynomial Band (PB). The representation has four polynomial curves to fit a text's top, bottom, left, and right sides, which can capture a text with a complex shape by varying polynomial coefficients. PB has appealing features compared with conventional representations: 1) It can model different curvatures with a fixed number of parameters, while polygon-points-based methods need to utilize a different number of points. 2) It can distinguish adjacent or overlapping texts as they have apparent different curve coefficients, while segmentation-based or points-based methods suffer from adhesive spatial positions. PBFormer combines the PB with the transformer, which can directly generate smooth text contours sampled from predicted curves without interpolation. A parameter-free cross-scale pixel attention (CPA) module is employed to highlight the feature map of a suitable scale while suppressing the other feature maps. The simple operation can help detect small-scale texts and is compatible with the one-stage DETR framework, where no postprocessing exists for NMS. Furthermore, PBFormer is trained with a shape-contained loss, which not only enforces the piecewise alignment between the ground truth and the predicted curves but also makes curves' positions and shapes consistent with each other. Without bells and whistles about text pre-training, our method is superior to the previous state-of-the-art text detectors on the arbitrary-shaped text datasets.Comment: 9 pages, 8 figures, accepted by ACM MM 202

Investigation of Carbon Tax Pilot in YRD Urban Agglomerations—Analysis and Application of a Novel ESER System with Carbon Tax Constraints

AbstractThis paper attempts to explore the dynamic behavior of energy-saving and emission-reduction (ESER) system in Yangtze River Delta (YRD) urban agglomerations, which has not yet been reported in present literature. The novel YRD urban agglomerations carbon tax attractor is achieved. A scenario study is carried out. The results show that, the ESER system in YRD urban agglomerations is superior to the average case in China, in which the impacts on economic growth are almost the same. The economic property of YRD urban agglomerations is the main cause why the ESER system of YRD urban agglomerations being superior